Create README.md

README.md

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+---
+tags:
+- biology
+---
+
+This dataset card contains data from the original [Basenji project](https://console.cloud.google.com/storage/browser/basenji_barnyard?inv=1&invt=AbzSKw). The original Basenji dataset has two main limitations:
+
+1. **Format**: Data is stored in TensorFlow format, which is not directly compatible with PyTorch workflows
+2. **Cost**: Users need to pay Google Cloud storage fees to download the data
+
+To facilitate PyTorch-based training, we have downloaded and converted the data to H5 format. With permission from the original Basenji authors, we are releasing the H5-formatted data here for free access.
+
+## 📁 Key Files
+
+- `human_train.h5`, `human_valid.h5`, `human_test.h5`
+- `mouse_train.h5`, `mouse_valid.h5`, `mouse_test.h5`
+
+## 📦 File Splitting & Reconstruction
+
+Since the training files exceed 50GB and cannot be directly uploaded to 🤗 Hugging Face, we split them using the following commands:
+
+```bash
+split -b 45G -d -a 2 human_train.h5 human_train_part_
+split -b 45G -d -a 2 mouse_train.h5 mouse_train_part_
+```
+
+After downloading all part files, you need to reconstruct the original H5 files:
+```
+# Reconstruct human_train.h5
+cat human_train_part_* > human_train.h5
+
+# Reconstruct mouse_train.h5  
+cat mouse_train_part_* > mouse_train.h5
+```
+
+## 📖 Citation
+If you find this dataset useful, please cite both the original Basenji paper and our work:
+```
+@article{kelley2018sequential,
+  title={Sequential regulatory activity prediction across chromosomes with convolutional neural networks},
+  author={Kelley, David R and Reshef, Yakir A and Bileschi, Maxwell and Belanger, David and McLean, Cory Y and Snoek, Jasper},
+  journal={Genome research},
+  volume={28},
+  number={5},
+  pages={739--750},
+  year={2018},
+  publisher={Cold Spring Harbor Lab}
+}
+
+@misc{yang2025spacegenomicprofilepredictor,
+  title={SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model}, 
+  author={Zhao Yang and Jiwei Zhu and Bing Su},
+  year={2025},
+  eprint={2506.01833},
+  archivePrefix={arXiv},
+  primaryClass={cs.LG},
+  url={https://arxiv.org/abs/2506.01833}
+}
+```